How to use get_path_ method in autotest

Best Python code snippet using autotest_python

urlmatcher.py

Source:urlmatcher.py

...69        for _, u in enumerate(matching_urls):70            parsed_u = urlparse(u)71            if parsed_u.scheme != parsed_url_to_match.scheme:72                continue73            url_path = get_path_(parsed_url_to_match)74            u_path = get_path_(parsed_u)75            if not should_use_matched_(url, u):76                continue77            score = compute_levenshtein_(url_path, u_path)78            # Levenshtein edit distance is the number of edits between the two79            # string. Thus, the smaller the better.80            if score < best_match_score:81                best_match = u82                best_match_score = score83        # Do a final sanity check that the match make sense.84        if best_match == NO_MATCH:85            return best_match, best_match_score86        return best_match, best_match_score87    def get_all_sift4_scores_(self, url, matching_urls):88        '''Get all matching scores for the given URL.'''89        parsed_url_to_match = urlparse(url)90        result = {}91        for _, u in enumerate(matching_urls):92            parsed_u = urlparse(u)93            if parsed_u.scheme != parsed_url_to_match.scheme:94                result[u] = DEFAULT_EDIT_DISTANCE95                continue96            if not should_use_matched_(url, u):97                result[u] = DEFAULT_EDIT_DISTANCE98                continue99            url_path = get_path_(parsed_url_to_match)100            u_path = get_path_(parsed_u)101            # Search for 500 tokens with a maximum of 200 distance.102            score = compute_sift4_(url_path, u_path, 5)103            result[u] = score104        return result105    def sift4_(self, url, matching_urls):106        '''This is based on Sift4 matching algorithm.'''107        best_match = NO_MATCH108        best_match_score = DEFAULT_EDIT_DISTANCE109        all_sift4_scores = self.get_all_sift4_scores_(url, matching_urls)110        for matching_url, score in all_sift4_scores.items():111            # Levenshtein edit distance is the number of edits between the two112            # string. Thus, the smaller the better.113            if not should_use_matched_(url, matching_url):114                continue115            parsed_matching_url = urlparse(matching_url)116            matching_url_path = get_path_(parsed_matching_url)117            if score < best_match_score:118                best_match = matching_url119                best_match_score = score120        return best_match, best_match_score121    def mahimahi_(self, url, matching_urls):122        '''This is based on Mahimahi's matching function.'''123        parsed_url_to_match = urlparse(url)124        url_to_match_without_query = '{scheme}://{netloc}{path}'.format(125                scheme=parsed_url_to_match.scheme,126                netloc=parsed_url_to_match.netloc,127                path=parsed_url_to_match.path)128        best_match = NO_MATCH129        best_match_score = -1130        for _, u in enumerate(matching_urls):131            parsed_u = urlparse(u)132            if parsed_u.scheme != parsed_url_to_match.scheme:133                continue134            if remove_query_(parsed_url_to_match) != remove_query_(parsed_u):135                continue136            score = min(len(url), len(u))137            for i in range(score):138                if url[i] != u[i]:139                    if i > best_match_score:140                        best_match = u141                        best_match_score = i142                        break143            if score > best_match_score:144                best_match = u145                best_match_score = score146        return best_match, best_match_score147def compute_levenshtein_(seq1, seq2):148    size_x = len(seq1) + 1149    size_y = len(seq2) + 1150    matrix = np.zeros ((size_x, size_y))151    for x in range(size_x):152        matrix [x, 0] = x153    for y in range(size_y):154        matrix [0, y] = y155    for x in range(1, size_x):156        for y in range(1, size_y):157            if seq1[x-1] == seq2[y-1]:158                matrix [x,y] = min(159                    matrix[x-1, y] + 1,160                    matrix[x-1, y-1],161                    matrix[x, y-1] + 1162                )163            else:164                matrix [x,y] = min(165                    matrix[x-1,y] + 1,166                    matrix[x-1,y-1] + 1,167                    matrix[x,y-1] + 1168                )169    return (matrix[size_x - 1, size_y - 1])170def compute_sift4_(seq1, seq2, max_offset, max_distance=0):171    '''172    Computes the distance between seq1 and seq2.173    Params:174        seq1: first string to compare175        seq2: second string to compare176        max_offset: number of positions of search for matching tokens177        max_distance: maximum distance between the two strings before giving up178    '''179    # Handle cases where seq1 or seq2 are invalid (None or empty)180    if seq1 is None or len(seq1) == 0:181        if seq2 is None:182            return 0183        return len(seq2)184    if seq2 is None or len(seq2) == 0:185        return len(seq1)186    # General case: both strings are valid and comparable.187    # var l1=s1.length;188    # var l2=s2.length;189    len_seq1 = len(seq1)190    len_seq2 = len(seq2)191    c_1 = 0192    c_2 = 0193    lcss = 0  # largest common subsequence194    local_cs = 0  # local common substring195    trans = 0  # number of transpositions ('ab' vs 'ba')196    offset_arr = []  # offset object: { "c1": c1, "c2": c2, "trans": trans }197    while (c_1 < len_seq1) and (c_2 < len_seq2):198        if seq1[c_1] == seq2[c_2]:199            local_cs += 1200            is_trans = False201            i = 0202            while i < len(offset_arr):  # see if current match is a transposition203                offset = offset_arr[i]204                if c_1 <= offset['c1'] or c_2 <= offset['c2']:205                    # when two matches cross, the one considered a transposition is the one with the largest difference in offsets206                    is_trans = abs(c_2 - c_1) >= abs(offset['c2'] - offset['c1'])207                    if is_trans:208                        trans += 1209                    else:210                        if not offset['trans']:211                            offset['trans'] = True212                            trans += 1;213                    break214                else:215                    if c_1 > offset['c2'] and c_2 > offset['c1']:216                        offset_arr.pop(0)217                    else:218                        i += 1219            offset_arr.append({220                'c1': c_1,221                'c2': c_2,222                'trans': is_trans,223            })224        else:225            lcss += local_cs226            local_cs = 0227            if c_1 != c_2:228                c_1 = min(c_1,c_2)229                c_2 = min(c_1,c_2)  # using min allows the computation of transposition230            # if matching characters are found, remove 1 from both cursors (they get incremented at the end of the loop)231            # so that we can have only one code block handling matches232            while i < max_offset and (c_1 + i < len_seq1 or c_2 + i < len_seq2):233                if (c_1 + i < len_seq1) and (seq1[c_1 + i] == seq2[c_2]):234                    c_1 += i - 1235                    c_2 -= 1236                    break237                if (c_2 + i < len_seq2) and (seq1[c_1] == seq2[c_2 + i]):238                    c_1 -= 1239                    c_2 += i - 1240                    break241                i += 1242        c_1 += 1243        c_2 += 1244        if max_distance > 0:245            temporary_dist = max(c_1,c_2) - (lcss - trans)246            if temporary_dist >= max_distance:247                return round(temporary_dist)248        # this covers the case where the last match is on the last token in list, so that it can compute transpositions correctly249        if (c_1 >= len_seq1) or (c_2 >= len_seq2):250            lcss += local_cs251            local_cs = 0252            c_1 = min(c_1, c_2)253            c_2 = min(c_1,c_2)254    lcss += local_cs255    return round(max(len_seq1, len_seq2) - (lcss - trans)) # remove half the number of transpositions from the lcss256def should_use_matched_(url1, url2):257    '''Checks if the given URLs have the same netloc.'''258    parsed_url1 = urlparse(url1)259    parsed_url2 = urlparse(url2)260    url1_path = parsed_url1.path261    url1_tokens = url1_path.split('/')262    num_url1_tokens = len(url1_tokens)263    url2_path = parsed_url2.path264    url2_tokens = url2_path.split('/')265    num_url2_tokens = len(url2_tokens)266    # return parsed_url1.netloc == parsed_url2.netloc and \267    #         num_url1_tokens == num_url2_tokens and \268    #         abs(len(url1_tokens[-1]) - len(url2_tokens[-1])) <= 2269    return parsed_url1.netloc == parsed_url2.netloc and \270            num_url1_tokens == num_url2_tokens271def remove_query_(parsed_url):272    '''Removes the query from the URL.'''273    retval = '{scheme}://{netloc}{path}'.format(274            scheme=parsed_url.scheme,275            netloc=parsed_url.netloc,276            path=get_path_(parsed_url))277    return retval278def get_path_(parsed_url):279    '''Returns the path and parameter.'''280    retval = parsed_url.path281    if parsed_url.params != '':282        retval += ';' + parsed_url.params283    return retval284def last_path_token_match_(parsed_u, parsed_url_to_match):285    '''Checks whether the last token of the path (ignoring the params) match.'''286    parsed_url_to_match_last_path_token = parsed_url_to_match.path.split('/')[-1]287    parsed_u_last_path_token = parsed_u.path.split('/')[-1]...

file_manager.py

Source:file_manager.py

...27    28    29    def delete(self):30        for file_name in self.get_delete_candidates_():31            file_path = self.get_path_(file_name, self.dst_dir, self.dst_extension)32            try:33                file_path.unlink()34                self.dst_file_names.remove(file_name)35            except FileNotFoundError as error:36                print(f'Error: file "{file_path}" does not found')37            finally:38                continue39    40    def get_path_(self, file_name, root_dir, extension):41        return root_dir / (file_name + '.' + extension)42    43    # def get_delete_candidates_(self):44    #     '''45    #     Doesnot work when src and dst is the same.46    #     '''47    #     for file_name in self.dst_file_names:48    #         if file_name not in self.src_file_names:49    #             yield file_name50    51    def get_delete_candidates_(self):52        candidates = []53        for file_name in self.dst_file_names:54            if file_name not in self.src_file_names:55                candidates.append(file_name)56        return candidates57                58                59    def random_copy(self, annot_dir, image_dir, ratio=0.2):60        file_names = list(self.src_file_names)61        62        count = math.ceil(ratio * len(file_names))63        indexes = np.random.permutation(len(file_names))64        indexes = indexes[:count]65        for i in indexes:66            annot_file_path = self.get_path_(file_names[i], self.src_dir, self.src_extension)67            img_file_path = self.get_path_(file_names[i], self.dst_dir, self.dst_extension)68            69            copyfile(annot_file_path, annot_dir / annot_file_path.name)...

test_com.py

Source:test_com.py

...3    file_name = 'test.csv'4    sub_dir = 'test_data'5    path = os.path.join(pathlib.Path(__file__).parent,sub_dir,file_name)6    return path7def get_path_(base_path:str,file_name:str='',sub_dir:str=''):8    import pathlib,os9    file_name = 'test.csv'10    sub_dir = 'test_data'11    if sub_dir != '':12        path = os.path.join(pathlib.Path(base_path).parent,sub_dir,file_name)13    else:14        path = os.path.join(pathlib.Path(base_path).parent,file_name)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.